Get an analysis of the problem statement for the social media sentiment analysis use case and review the goals to achieve while architecting the solution.
- [Instructor] Let us now take a look at our first Use Case in real time architectures. Social Media Sentiment Analysis. More and more customers today are using social media to talk about products and services. These posted opinions can go viral and impact a business's reputation. Hence, businesses are focusing on analyzing these posts in real-time and take action before things get out of control.
So, here is the problem for you to solve. Your company's customers are using social media to express their feedback on your company's website and products. There are more than 100,000 posts on different social media websites everyday. Some comments by your customers have gained a lot of publicity of late and have created a negative impact on your business's brand.
So, your business wants to track these posts and the posters on social media, in real-time, and take action before things get worse. Hence, your business needs a real-time social media tracking system to identify negative posts and alert your customer service. So here is your objective for this Use Case. Architect a real-time social media sentiment analysis system to identify negative posts and alert customer support.
The goals for this architecture are: provide real-time monitoring of social media. Real-time in this Use Case would be a few minutes. Scale horizontally to accommodate future growth in posts and additional analytics. Provide real-time summary for the overall social media sentiment. Provide for capability to add more social media channels, like Instagram, in the future.
There is no coding involved. Instead you will see how big data tools can help solve some of the most complex challenges for businesses that generate, store, and analyze large amounts of data. The use cases are drawn from a variety of industries, including ecommerce and IT. Instructor Kumaran Ponnambalam shows how to analyze a problem, draw an architectural outline, choose the right technologies, and finalize the solution. After each use case, he reviews related best practices for real-time streaming, predictive analytics, parallel processing, and pipeline management. Each lesson is rich in practical techniques and insights from a developer who has experienced the benefits and shortcomings of these technologies firsthand.
- Components of a big data application
- Big data app development strategies
- Use cases: fraud detection and product recommendations
- Technology options
- Designing solutions
- Best practices